AI UNIT 1 • FOUNDATIONS • FREE

What Is AI?

Everyone is talking about AI. Before you use it, build with it, or trust it, you should know what it actually is, and what it means for a machine to "learn." We start where our people have always started: with story.

🪶 5 Stages
🌱 Foundations · No coding
🎯 30-45 min per stage
💬 Guided AI Sandbox

The Big Idea

In our communities, knowledge is passed down. You learn by listening, watching, practicing, and remembering, and the knowledge carries meaning, responsibility, and a source you can name.

An AI "learns" in a very different way. It is shown enormous piles of text and images, millions of examples, and it finds patterns in them. It does not understand what it reads, and it cannot tell you who taught it or whether they were right. When you ask it a question, it predicts the words that usually come next. That is powerful. It is also very different from knowing something.

In this unit you will talk with a real AI for the first time in a safe, guided sandbox, no coding required. You will ask it about your own nation and community, write down what it gets right and what it gets wrong, and start building the single most important habit for the rest of this track: thinking critically about what AI tells you.

💬 How this unit works. Instead of a code editor, your workspace has an Ask the AI panel and a Field Notes panel. You'll type prompts, read the AI's answers, and record what you notice. Everything you write in Field Notes is saved for you and visible to your teacher.

By the end of this unit, you'll be able to say "I can..."

  • Explain in my own words what AI is and what it means for a machine to "learn"
  • Describe how learning from data is different from how my community passes down knowledge
  • Write a prompt, read the AI's response, and keep useful notes on what I find
  • Point to at least one thing an AI does well and one thing it gets wrong about Native peoples
  • Explain why what an AI was trained on shapes what it can and cannot say

What You'll Make

Not a website this time, a set of Field Notes: your own record of what happened when you tested an AI.

You'll log the prompts you tried, what the AI answered about your nation and community, where it was accurate, and where it was wrong, vague, or stereotyped. You'll finish with a short reflection comparing how a machine learns to how knowledge is carried in your own family and community.

Those notes are the foundation for every AI unit that follows, and they're evidence, in your own words, that you can think clearly about a tool the whole world is still figuring out.

Your learning path

The 5 Stages

STAGE 1 Meet the Machine Your first conversation with an AI STAGE 2 Ask It About Your Nation Test what it knows, and doesn't STAGE 3 Where Answers Come From Training data, in plain language STAGE 4 Right, Wrong & In Between Spotting errors and stereotypes STAGE 5 Your Field Notes Reflect & compare to how we know
Let's Begin →

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Sources

The AI concepts in this unit are grounded in the leading K-12 AI education frameworks, and the Native-centered framing is grounded in Indigenous data governance scholarship. We prioritize Native-led organizations, peer-reviewed research, and the national bodies that write AI education standards. Every source below is a trustworthy starting point for educators and students who want to go deeper.

AI Literacy & Education

Indigenous Data & Knowledge

Digital Citizenship

Educational Standards

This unit aligns with the national AI education framework (AI4K12), Indigenous data governance principles (CARE), and national and state computer science, media literacy, and social studies standards. Open any section below to see how the unit meets it.

  • Big Idea 3, Learning (Grades 6-12): Computers can learn from data. (The unit's entire Big Idea is what it means for a machine to "learn" from examples rather than understand. Students contrast pattern-matching over data with human, community-based ways of coming to know something.)
  • Big Idea 4, Natural Interaction (Grades 5-12): Intelligent agents require many kinds of knowledge to interact naturally with humans. (Students hold their first real conversation with an AI in the sandbox, experiencing directly how it responds to natural-language prompts, and where that interaction breaks down.)
  • Big Idea 5, Societal Impact (Grades 5-12): AI can impact society in both positive and negative ways. (By testing what the AI says about their own nation, students encounter first-hand how AI can misrepresent or erase communities, grounding "societal impact" in their own lived experience rather than the abstract.)
  • Authority to Control (A): Indigenous peoples' rights and interests in their data and knowledge must be recognized. (Students investigate who gets to decide what an AI says about their nation, and begin to see that when a model speaks about a community without that community's authority, something is missing.)
  • Responsibility (R): Those working with Indigenous data have a responsibility to support Indigenous communities. (Students practice a foundational responsibility, verifying claims against real sources instead of trusting AI output, and learn to name the source of what they know.)
  • Ethics (E): Indigenous peoples' rights and wellbeing should be the primary concern across the data life cycle. (The unit models an ethic of accuracy and respect: students document harm (stereotypes, erasure) rather than repeat it, treating representation of their people as a matter of ethics, not opinion.)
  • OSEU 6, Indigenous Ways of Knowing: The Oceti Sakowin have a rich oral tradition through which knowledge, values, and history are passed between generations. (The unit explicitly contrasts oral, relational ways of knowing with how an AI is trained, honoring community knowledge-keeping as a distinct and valid way of knowing.)
  • OSEU 7, Learning & Identity: Understanding and language shape identity and worldview. (Students examine how an AI describes their people and consider how misrepresentation online can shape how others, and even young Native people, see their communities.)
  • OSEU 2, Sovereignty: Tribal nations are sovereign and have the right to self-representation. (Testing how a machine represents a nation without its input introduces the idea that self-representation is part of sovereignty, setting up the data-sovereignty units later in the track.)
  • CSTA 2-IC-21 (Grades 6-8): Discuss issues of bias and accessibility in the design of existing technologies. (Students directly investigate bias in an AI system by testing its output about Native peoples and documenting where it falls short.)
  • CSTA 2-IC-22 (Grades 6-8): Collaborate with contributors when creating content and giving credit. (Students practice naming and crediting real sources when they verify AI claims, building the habit of attribution the AI itself cannot provide.)
  • ISTE 1.2, Digital Citizen: Students recognize the rights, responsibilities, and opportunities of living in a digital world, and act in ways that are safe, legal, and ethical. (The unit builds critical, responsible AI use from the very first prompt, in a guarded sandbox with teacher visibility.)
  • ISTE 1.3, Knowledge Constructor: Students critically curate resources and evaluate the accuracy and relevance of information. (Students evaluate the accuracy of AI-generated claims against verified sources, the core skill of the unit.)
  • MN ELA, Reading Informational Text / Evaluating Sources (Grades 6-8): Evaluate the argument and claims in a text, assessing whether reasoning is sound and evidence is relevant and sufficient. (Students treat AI output as a source to be evaluated, checking its claims about Tribal nations against verified references.)
  • MN Media Arts 2.6.5.10.1 / 2.7.5.10.1 / 2.8.5.10.1 (Grades 6-8): Demonstrate an understanding that media artworks influence and are influenced by personal, societal, cultural, and historical contexts, including the contributions of Minnesota American Indian Tribes and communities. (Students analyze how AI-generated media represents, and misrepresents, MN Tribal nations and communities.)
  • MN Social Studies, Citizenship & Government (Grades 6-8): Analyze how people access, use, and evaluate information to participate as informed members of a community. (The unit builds the informed-citizen skill of questioning an increasingly common information source.)
  • Note for RF: Minnesota's revised K-12 standards and emerging state AI guidance should be cross-checked and the exact codes localized before publishing.
  • ND CS 6.S.1 / 7.S.1 / 8.S.1 (Grades 6-8): Examine the positive and negative impacts of technology on how people live, work, and interact, including considerations of equitable access. (Students weigh how AI's representation of Native peoples affects communities, connecting computing impact to Tribal visibility and equity.)
  • ND Indigenous Language Standard 2.1 (All Grades): Learners investigate, explain, and reflect on the relationship of practices to the customs, traditions, and perspectives of the cultures studied. (Students reflect on how community knowledge-keeping differs from machine learning, honoring Indigenous ways of knowing.)
  • ND ELA, Evaluating Information & Sources (Grades 6-8): Assess the credibility and accuracy of sources. (AI output is treated as a source whose credibility must be tested, not assumed.)
  • Note for RF: verify exact ND CS and ELA codes against the current published frameworks before publishing.
  • OSEU Standard 6 (All Grades): Understand Oceti Sakowin ways of knowing, including oral tradition. (The unit centers oral, relational knowledge-keeping as a distinct way of knowing, in direct contrast to how AI learns from data.)
  • OSEU Standard 2 (All Grades): Understand the concept of sovereignty. (Self-representation is introduced as part of sovereignty when students see a machine describe a nation without its input.)
  • SD CS 6-8.IC.01 (Grades 6-8): Compare tradeoffs of computing technologies that affect everyday activities, including urban, rural, and reservation communities. (This standard explicitly names reservation communities; students consider how AI shapes visibility and information for Native communities.)
  • Note for RF: verify exact SD CS codes and OSEU numbering against the current published frameworks before publishing.